Validation of Two‐Dimensional Methods for Left Atrial Volume Measurement: A Comparison of Echocardiography with Cardiac Computed Tomography
Bibliographic record
Abstract
BACKGROUND: Left atrial volume (LAVol) is an important predictor of cardiovascular outcomes. Different formulas are applied to calculate LAVol using two-dimensional transthoracic echocardiography (2DTTE) with variable reference values. The objective of the study was to evaluate the accuracy of methods to calculate LAVol by 2DTTE or cardiac computed tomography (CT). METHODS AND RESULTS: Overall 177 consecutive patients who underwent both a 2DTTE and retrospective electrocardiogram (ECG)-gated coronary CT angiography (CTA) within 15 days were included for this study. LA volume measurements were calculated by 2DTTE and 2DCT using the biplane area-length, biplane Simpson's, prolate-ellipsoid-1 and prolate-ellipsoid-2 methods. These results were compared with those measured by CT using a volumetric method. There was very good correlation between the CT and echocardiographic measures for LAVol, but significant underestimation of the echocardiographic methods when compared to the reference standard (33.5%, 39.1%, 48.1%, and 53.2% for the biplane area-length, biplane Simpson's, prolate-ellipsoid-1, and prolate-ellipsoid-2 methods, respectively). The biplane area-length method using 2DTTE had the closest volume estimation of all echocardiographic methods to the reference standard (67.6 ± 25.5 mL vs. 106 ± 35.5 mL, r = 0.712). Similarly, the biplane area-length method using CT most accurately predicted LAVol (103.3 ± 36.0 mL, r = 0.965). CONCLUSIONS: Compared to CT, 2DTTE provides reasonable assessment of LAVol, although all measurement methods underestimate LAVol. For both 2DTTE and CT, the biplane area-length method appears to provide the most accurate 2D estimate of LAVol.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".